Before a computer can interpret speech, the raw audio signal must be transformed into a structured, numerical representation. Machine learning models do not operate directly on sound waves; they require features that isolate the acoustic properties relevant to speech. This chapter covers the standard procedures for performing this transformation.

We will start with the fundamentals of how continuous sound is digitized through sampling and quantization. Then, you will learn to visualize these signals as waveforms and spectrograms to inspect their content. The main part of the chapter concentrates on feature extraction, walking through the steps to generate Mel-Frequency Cepstral Coefficients (MFCCs), a common input for ASR systems.

Specifically, you will learn to:

• Represent sound digitally using sampling and quantization.
• Visualize audio data with waveforms and spectrograms.
• Apply preprocessing steps like pre-emphasis, framing, and windowing.
• Calculate MFCCs from a raw audio signal.
• Process an audio file into features using Python.

By the end of this chapter, you will be able to take a standard audio file and convert it into a feature matrix suitable for use with an acoustic model.